In the meantime, Chomsky (1965) devised a paradigm for linguistic analysis that includes syntactic, semantic, and phonological components to account for the generation of natural language statements. This theory can be interpreted to imply that the meaning of a sentence can be represented as a semantically interpreted deep structure--i.e, From computer science's preoccupation with formal programming languages and compilers, there emerged another paradigm. The adoption and combination of these two new paradigms have resulted in a vigorous new generation of language processing systems characterized by sophisticated linguistic and logical processing of well-defined formal data structures. These included a social-conversation machine, systems that translated from English into limited logical calculi, and programs that attempted to answer questions from English text.
This paper is a survey of the current machine translation research in the US, Europe and Japan. A short history of machine translation is presented first, followed by an overview of the current research work. Representative examples of a wide range of different approaches adopted by machine translation researchers are presented. In support of this discussion, issues in, and techniques for, evaluating machine translation systems are addressed.
Much of classical and contemporary analysis stems from this source: iteration, ergodic theory, the theory of semigroups , the theory of branching processes , random transformations at fixed times and deterministic transformations at stochastic times [3, 4]. Let us now describe a dynamic programming process of discrete, deterministic type. This is an extremely important observation since it enables us to employ a type of approximation not available in classical analysis, approximation in policy space. A particular class of problems of this type involves ordinary and partial differential operators and is related both to the theory of differential inequalities inaugurated by Caplygin and Lyapunoy [15, 16], and to the modern maximum principles of partial differential equations.
The program, which is written in the LISP language, uses heuristic methods to calculate, from relatively primitive representations of the input figures, descriptions of these figures in terms of subfigures and relations among them. It then utilizes these descriptions to find an appropriate rule and to apply it, modified as necessary, to arrive at an answer. The program solved a large number of such problems, including many taken directly from college-level intelligence tests. The novel organization of the program in terms of figure descriptions, which are analyzed to find transformation rules, and rule descriptions, which are analyzed to find'common generalizations' of pairs of transformation rules, has implications for the design of problem-solving programs and for machine learning.
A large high-speed general-purpose digital computer (IBM 7090) wasProgrammed to solve elementary symbolic integration problems at approximatelythe level of a good college freshman. The program is called SAINT,an acronym for "Symbolic Automatic INTegrator." The SAINT programis written in LISP (McCarthy, 1960), and most of the work reported hereJs the substance of a doctoral dissertation at the Massachusetts Institute ofTechnology (Slagle, 1961). This discussion concerns the SAINT programand its performance.Some typical samples of SAINT's external behavior are given so thatthe reader may think in concrete terms. Journal of the ACM, Vol 10, No. 4, pp. 507-520, October 1963.
Secondly, he might spend too much time on reading irrelevant material. Or rather, how does one set up a set of buttons so that, through selection of an appropriate subset of these buttons, the list of relevant references will be presented? We are now back to the bread-and-butter problem of improving extant literature searching methods and, during our preliminary discussion we seem to have entirely lost sight of the issue indicated by the first word of the title of this paper, "mechanization". Some 10 years ago, electronic computers made their sensational abut and proved themselves able to solve computational problems at speeds that were many degrees of order higher than those attainable by humans.
Particular attention is given to processes involving pattern recognition, learning, planning ahead, and the use of analogies or?models!. Second, we can often find simple machines which in certain situations do exhibit performances which would be called intelligent If done by a man. In attempting to design intelligent machines we are, in effect, concerned with the problems of "creativity". Usually the problem is not so much to find the basic structure (or the domain of things to try) as to find ways of reducing this structure to reasonable size.